kotaemon: what it is, what problem it solves & why it's gaining traction

What it solves

Kotaemon is an open-source RAG (Retrieval-Augmented Generation) UI that allows users to chat with their documents. It bridges the gap between end users who need a clean interface for document QA and developers who want a customizable framework to build and test their own RAG pipelines.

How it works

The system uses a hybrid RAG pipeline combining full-text and vector retrieval with re-ranking to optimize answer quality. It supports various LLM providers (OpenAI, Azure, Groq) and local models via Ollama or llama-cpp-python. For document processing, it offers multi-modal parsing (OCR, table, and figure extraction) and provides detailed citations with an in-browser PDF viewer that highlights relevant sections.

Who it’s for

• End users looking for a user-friendly way to perform QA on their private or public document collections.
• Developers who want a framework to build, customize, and deploy RAG pipelines using a Gradio-based UI.

Highlights

• Hybrid Retrieval: Combines full-text and vector search with re-ranking.
• Advanced Citations: In-browser PDF viewer with highlights and relevance scores.
• Multi-modal Support: Handles documents with figures and tables using various local and API-based loaders.
• Complex Reasoning: Supports question decomposition and agent-based reasoning (e.g., ReAct, ReWOO).
• Flexible Deployment: Available via Docker (lite/full/ollama versions) or local Python installation.